Gesture Based Control Using Three-Dimensional Information Extracted Over an Extended Depth of Field

ABSTRACT

Systems and methods are described for gesture-based control using three-dimensional information extracted over an extended depth of field. The system comprises a plurality of optical detectors coupled to at least one processor. The optical detectors image a body. At least two optical detectors of the plurality of optical detectors comprise wavefront coding cameras. The processor automatically detects a gesture of the body, wherein the gesture comprises an instantaneous state of the body. The detecting comprises aggregating gesture data of the gesture at an instant in time. The gesture data includes focus-resolved data of the body within a depth of field of the imaging system. The processor translates the gesture to a gesture signal, and uses the gesture signal to control a component coupled to the processor.

RELATED APPLICATION

This application is a continuation in part of U.S. patent applicationSer. No. 11/350,697, filed Feb. 8, 2006.

This application claims the benefit of U.S. patent application Ser. No.61/041,892, filed Apr. 2, 2008.

This application is a continuation in part of U.S. patent applicationSer. No. 12/109,263, filed Apr. 24, 2008.

This application claims the benefit of U.S. patent application Ser. No.61/105,243, filed Oct. 14, 2008.

This application claims the benefit of U.S. patent application Ser. No.61/105,253, filed Oct. 14, 2008.

FIELD OF THE INVENTION

This invention relates to the field of computer system in general and inparticular to a system and method for a gesture based control systemusing extraction of three-dimensional information over an extended depthof field.

BACKGROUND

When extracting three-dimensional information over an extended depth offield in imaging systems, distance to a point in a scene can beestimated from its location in two or more images capturedsimultaneously. The three dimensional (3D) position of the point can becomputed from basic geometric relationships when the 3D relationshipbetween the imagers is known. The challenge in computing spatiallocation from multiple images, often referred to as stereo correlationor stereo depth computation, is automatically and accurately associatingthe mapping of a point in one image with its mapping in another image.This is most often done by correlating image features from one image toone or more others. The underlying assumption in all stereo matchingmethods, however, is that there must be some identifiable local contrastor feature in the image in order to match that point to its location inanother image. Therefore a problem arises when there is no localcontrast or feature in the image because of misfocus—stereo matchingdoes not produce accurate results in regions of an image that are out offocus.

The conventional means for extending the focal depth of an image is toreduce the diameter of the camera's lens's pupil (“stopping down”).However, two side effects restrict the usefulness of the technique.First, the sensitivity of the imaging system is reduced by a factorequal to the square of the pupil diameter ratio. Second, the maximumspatial frequency response is reduced a factor equal to the pupildiameter ratio, which limits the resolution and contrast in the image.There is thus a tradeoff between depth of field, exposure time, andoverall contrast in conventional imaging systems. In the case of amultiple camera ranging system, the net effect will be a compromisebetween stereoscopic depth accuracy and working range.

INCORPORATION BY REFERENCE

Each patent, patent application, and/or publication mentioned in thisspecification is herein incorporated by reference in its entirety to thesame extent as if each individual patent, patent application, and/orpublication was specifically and individually indicated to beincorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an embodiment of the system of the invention.

FIG. 2 is a diagram of an embodiment of marking tags of the invention.

FIG. 3 is a diagram of poses in a gesture vocabulary in an embodiment ofthe invention.

FIG. 4 is a diagram of orientation in a gesture vocabulary in anembodiment of the invention.

FIG. 5 is a diagram of two hand combinations in a gesture vocabulary inan embodiment of the invention.

FIG. 6 is a diagram of orientation blends in a gesture vocabulary in anembodiment of the invention.

FIG. 7 is a flow diagram illustrating the operation an embodiment of thesystem of the invention.

FIG. 8 is an example of commands in an embodiment of the system.

FIG. 9 is a block diagram of gesture-based control system for extractingthree-dimensional information over an extended depth of field, under anembodiment.

FIG. 10 is a block diagram of a wavefront coding imaging system used ina gesture-based control system, under an embodiment.

FIG. 11 is a block diagram of gesture-based control system forextracting three-dimensional information over an extended depth of fieldusing a wavefront coding imaging system that includes two wavefrontcoding cameras, under an embodiment.

FIG. 12 is a flow diagram for gesture-based control usingthree-dimensional information extracted over an extended depth of field,under an embodiment.

FIG. 13 is a block diagram of a wavefront coding design process used ina gesture-based control system, under an embodiment.

DETAILED DESCRIPTION

Systems and methods are described below for gesture-based control usingthree-dimensional information extracted over an extended depth of field.A system of an embodiment comprises a plurality of optical detectorscoupled to at least one processor. The optical detectors image a body.At least two optical detectors of the plurality of optical detectorscomprise wavefront coding cameras. The processor automatically detects agesture of the body, wherein the gesture comprises an instantaneousstate of the body. The detecting comprises aggregating gesture data ofthe gesture at an instant in time. The gesture data includesfocus-resolved data of the body within a depth of field of the imagingsystem. The processor translates the gesture to a gesture signal, anduses the gesture signal to control a component coupled to the processor.

A method of an embodiment comprises imaging a body with an imagingsystem, wherein the imaging includes generating wavefront coded imagesof the body. The method automatically detects a gesture of a body,wherein the gesture comprises an instantaneous state of the body. Thedetecting comprises aggregating gesture data of the gesture at aninstant in time. The gesture data includes focus-resolved data of thebody within a depth of field of the imaging system. The method comprisestranslating the gesture to a gesture signal, and controlling a componentcoupled to a computer in response to the gesture signal.

In the following description, a number of features are described indetail in order to provide a more thorough understanding of theembodiments described herein. It is apparent that the invention may bepracticed without these specific details. In other cases, well knownfeatures have not been described in detail.

System

A block diagram of an embodiment of the invention is illustrated inFIG. 1. A user locates his hands 101 and 102 in the viewing area of anarray of cameras 104A-104D. The cameras detect location, orientation,and movement of the fingers and hands 101 and 102 and generate outputsignals to pre-processor 105. Pre-processor 105 translates the cameraoutput into a gesture signal that is provided to the computer processingunit 107 of the system. The computer 107 uses the input information togenerate a command to control one or more on screen cursors and providesvideo output to display 103.

Although the system is shown with a single user's hands as input, theinvention may also be implemented using multiple users. In addition,instead of or in addition to hands, the system may track any part orparts of a user's body, including head, feet, legs, arms, elbows, knees,and the like.

In the embodiment shown, four cameras are used to detect the location,orientation, and movement of the user's hands 101 and 102. It should beunderstood that the invention may be used with more or fewer cameraswithout departing from the scope or spirit of the invention. Inaddition, although the cameras are disposed symmetrically in the exampleembodiment, there is no requirement of such symmetry in the invention.Any number or positioning of cameras that permits the location,orientation, and movement of the user's hands may be used in theinvention.

In one embodiment of the invention, the cameras used are motion capturecameras capable of capturing grey-scale images. In one embodiment, thecameras used are those manufactured by Vicon, such as the Vicon MX40camera. This camera includes on-camera processing and is capable ofimage capture at 1000 frames per second. A motion capture camera iscapable of detecting and locating markers.

In the embodiment described, the cameras are used for optical detection.In other embodiments, the cameras or other detectors may be used forelectromagnetic, magnetostatic, RFID, or any other suitable type ofdetection.

Pre-processor 105 is used to generate three dimensional space pointreconstruction and skeletal point labeling. The gesture translator 106is used to convert the 3D spatial information and marker motioninformation into a command language that can be interpreted by acomputer processor to update the location, shape, and action of a cursoron a display. In an alternate embodiment of the invention, thepre-processor 105 and gesture translator 106 can be combined into asingle device.

Computer 107 may be any general purpose computer such as manufactured byApple, Dell, or any other suitable manufacturer. The computer 107 runsapplications and provides display output. Cursor information that wouldotherwise come from a mouse or other prior art input device now comesfrom the gesture system.

Marker Tags

The invention contemplates the use of marker tags on one or more fingersof the user so that the system can locate the hands of the user,identify whether it is viewing a left or right hand, and which fingersare visible. This permits the system to detect the location,orientation, and movement of the users hands. This information allows anumber of gestures to be recognized by the system and used as commandsby the user.

The marker tags in one embodiment are physical tags comprising asubstrate (appropriate in the present embodiment for affixing to variouslocations on a human hand) and discrete markers arranged on thesubstrate's surface in unique identifying patterns.

The markers and the associated external sensing system may operate inany domain (optical, electromagnetic, magnetostatic, etc.) that allowsthe accurate, precise, and rapid and continuous acquisition of theirthree-space position. The markers themselves may operate either actively(e.g. by emitting structured electromagnetic pulses) or passively (e.g.by being optically retroreflective, as in the present embodiment).

At each frame of acquisition, the detection system receives theaggregate cloud of recovered three-space locations comprising allmarkers from tags presently in the instrumented workspace volume (withinthe visible range of the cameras or other detectors). The markers oneach tag are of sufficient multiplicity and are arranged in uniquepatterns such that the detection system can perform the following tasks:(1) segmentation, in which each recovered marker position is assigned toone and only one subcollection of points that form a single tag; (2)labelling, in which each segmented subcollection of points is identifiedas a particular tag; (3) location, in which the three-space position ofthe identified tag is recovered; and (4) orientation, in which thethree-space orientation of the identified tag is recovered. Tasks (1)and (2) are made possible through the specific nature of themarker-patterns, as described below and as illustrated in one embodimentin FIG. 2.

The markers on the tags in one embodiment are affixed at a subset ofregular grid locations. This underlying grid may, as in the presentembodiment, be of the traditional Cartesian sort; or may instead be someother regular plane tessellation (a triangular/hexagonal tilingarrangement, for example). The scale and spacing of the grid isestablished with respect to the known spatial resolution of themarker-sensing system, so that adjacent grid locations are not likely tobe confused. Selection of marker patterns for all tags should satisfythe following constraint: no tag's pattern shall coincide with that ofany other tag's pattern through any combination of rotation,translation, or mirroring. The multiplicity and arrangement of markersmay further be chosen so that loss (or occlusion) of some specifiednumber of component markers is tolerated: After any arbitrarytransformation, it should still be unlikely to confuse the compromisedmodule with any other.

Referring now to FIG. 2, a number of tags 201A-201E (left hand) and202A-202E (right hand) are shown. Each tag is rectangular and consistsin this embodiment of a 5×7 grid array. The rectangular shape is chosenas an aid in determining orientation of the tag and to reduce thelikelihood of mirror duplicates. In the embodiment shown, there are tagsfor each finger on each hand. In some embodiments, it may be adequate touse one, two, three, or four tags per hand. Each tag has a border of adifferent grey-scale or color shade. Within this border is a 3×5 gridarray. Markers (represented by the black dots of FIG. 2) are disposed atcertain points in the grid array to provide information.

Qualifying information may be encoded in the tags' marker patternsthrough segmentation of each pattern into ‘common’ and ‘unique’subpatterns. For example, the present embodiment specifies two possible‘border patterns’, distributions of markers about a rectangularboundary. A ‘family’ of tags is thus established—the tags intended forthe left hand might thus all use the same border pattern as shown intags 201A-201E while those attached to the right hand's fingers could beassigned a different pattern as shown in tags 202A-202E. This subpatternis chosen so that in all orientations of the tags, the left pattern canbe distinguished from the right pattern. In the example illustrated, theleft hand pattern includes a marker in each corner and on marker in asecond from corner grid location. The right hand pattern has markers inonly two corners and two markers in non corner grid locations. Aninspection of the pattern reveals that as long as any three of the fourmarkers are visible, the left hand pattern can be positivelydistinguished from the left hand pattern. In one embodiment, the coloror shade of the border can also be used as an indicator of handedness.

Each tag must of course still employ a unique interior pattern, themarkers distributed within its family's common border. In the embodimentshown, it has been found that two markers in the interior grid array aresufficient to uniquely identify each of the ten fingers with noduplication due to rotation or orientation of the fingers. Even if oneof the markers is occluded, the combination of the pattern and thehandedness of the tag yields a unique identifier.

In the present embodiment, the grid locations are visually present onthe rigid substrate as an aid to the (manual) task of affixing eachretroreflective marker at its intended location. These grids and theintended marker locations are literally printed via color inkjet printeronto the substrate, which here is a sheet of (initially) flexible‘shrink-film’. Each module is cut from the sheet and then oven-baked,during which thermal treatment each module undergoes a precise andrepeatable shrinkage. For a brief interval following this procedure, thecooling tag may be shaped slightly—to follow the longitudinal curve of afinger, for example; thereafter, the substrate is suitably rigid, andmarkers may be affixed at the indicated grid points.

In one embodiment, the markers themselves are three dimensional, such assmall reflective spheres affixed to the substrate via adhesive or someother appropriate means. The three dimensionality of the markers can bean aid in detection and location over two dimensional markers. Howevereither can be used without departing from the spirit and scope of thepresent invention.

At present, tags are affixed via Velcro or other appropriate means to aglove worn by the operator or are alternately affixed directly to theoperator's fingers using a mild double-stick tape. In a thirdembodiment, it is possible to dispense altogether with the rigidsubstrate and affix—or ‘paint’—individual markers directly onto theoperator's fingers and hands.

Gesture Vocabulary

The invention contemplates a gesture vocabulary consisting of handposes, orientation, hand combinations, and orientation blends. Anotation language is also implemented for designing and communicatingposes and gestures in the gesture vocabulary of the invention. Thegesture vocabulary is a system for representing instantaneous ‘posestates’ of kinematic linkages in compact textual form. The linkages inquestion may be biological (a human hand, for example; or an entirehuman body; or a grasshopper leg; or the articulated spine of a lemur)or may instead be nonbiological (e.g. a robotic arm). In any case, thelinkage may be simple (the spine) or branching (the hand). The gesturevocabulary system of the invention establishes for any specific linkagea constant length string; the aggregate of the specific ASCII charactersoccupying the string's ‘character locations’ is then a uniquedescription of the instantaneous state, or ‘pose’, of the linkage.

Hand Poses

FIG. 3 illustrates hand poses in an embodiment of a gesture vocabularyusing the invention. The invention supposes that each of the fivefingers on a hand are used. These fingers are codes as p-pinkie, r-ringfinger, m-middle finger, i-index finger, and t-thumb. A number of posesfor the fingers and thumbs are defined and illustrated in FIG. 3. Agesture vocabulary string establishes a single character position foreach expressible degree of freedom in the of the linkage (in this case,a finger). Further, each such degree of freedom is understood to bediscretized (or ‘quantized’), so that its full range of motion can beexpressed through assignment of one of a finite number of standard ASCIIcharacters at that string position. These degrees of freedom areexpressed with respect to a body-specific origin and coordinate system(the back of the hand, the center of the grasshopper's body; the base ofthe robotic arm; etc.). A small number of additional gesture vocabularycharacter positions are therefore used to express the position andorientation of the linkage ‘as a whole’ in the more global coordinatesystem.

Still referring to FIG. 3, a number of poses are defined and identifiedusing ASCII characters. Some of the poses are divided between thumb andnon-thumb. The invention in this embodiment uses a coding such that theASCII character itself is suggestive of the pose. However, any charactermay used to represent a pose, whether suggestive or not. In addition,there is no requirement in the invention to use ASCII characters for thenotation strings. Any suitable symbol, numeral, or other representationmaybe used without departing from the scope and spirit of the invention.For example, the notation may use two bits per finger if desired or someother number of bits as desired.

A curled finger is represented by the character “̂” while a curled thumbby “>”. A straight finger or thumb pointing up is indicated by “1” andat an angle by “\” or “/”. “−” represents a thumb pointing straightsideways and “×” represents a thumb pointing into the plane.

Using these individual finger and thumb descriptions, a robust number ofhand poses can be defined and written using the scheme of the invention.Each pose is represented by five characters with the order beingp-r-m-i-t as described above. FIG. 3 illustrates a number of poses and afew are described here by way of illustration and example. The hand heldflat and parallel to the ground is represented by “11111”. A fist isrepresented by “̂̂̂̂>”. An “OK” sign is represented by “111̂>”.

The character strings provide the opportunity for straightforward ‘humanreadability’ when using suggestive characters. The set of possiblecharacters that describe each degree of freedom may generally be chosenwith an eye to quick recognition and evident analogy. For example, avertical bar (‘|’) would likely mean that a linkage element is‘straight’, an ell (‘L’) might mean a ninety-degree bend, and acircumflex (‘̂’) could indicate a sharp bend. As noted above, anycharacters or coding may be used as desired.

Any system employing gesture vocabulary strings such as described hereinenjoys the benefit of the high computational efficiency of stringcomparison—identification of or search for any specified pose literallybecomes a ‘string compare’ (e.g. UNIX's ‘strcmp( )’ function) betweenthe desired pose string and the instantaneous actual string.Furthermore, the use of ‘wildcard characters’ provides the programmer orsystem designer with additional familiar efficiency and efficacy:degrees of freedom whose instantaneous state is irrelevant for a matchmay be specified as an interrogation point (‘?’); additional wildcardmeanings may be assigned.

Orientation

In addition to the pose of the fingers and thumb, the orientation of thehand can represent information. Characters describing global-spaceorientations can also be chosen transparently: the characters ‘<’, ‘>’,‘̂’, and ‘v’ may be used to indicate, when encountered in an orientationcharacter position, the ideas of left, right, up, and down. FIG. 4illustrates hand orientation descriptors and examples of coding thatcombines pose and orientation. In an embodiment of the invention, twocharacter positions specify first the direction of the palm and then thedirection of the fingers (if they were straight, irrespective of thefingers' actual bends). The possible characters for these two positionsexpress a ‘body-centric’ notion of orientation: ‘−’, ‘+’, ‘×’, ‘*’, ‘̂’,and ‘v’ describe medial, lateral, anterior (forward, away from body),posterior (backward, away from body), cranial (upward), and caudal(downward).

In the notation scheme of and embodiment of the invention, the fivefinger pose indicating characters are followed by a colon and then twoorientation characters to define a complete command pose. In oneembodiment, a start position is referred to as an “xyz” pose where thethumb is pointing straight up, the index finger is pointing forward andthe middle finger is perpendicular to the index finger, pointing to theleft when the pose is made with the right hand. This is represented bythe string “̂̂x1-:-X”.

‘XYZ-hand’ is a technique for exploiting the geometry of the human handto allow full six-degree-of-freedom navigation of visually presentedthree-dimensional structure. Although the technique depends only on thebulk translation and rotation of the operator's hand—so that its fingersmay in principal be held in any pose desired—the present embodimentprefers a static configuration in which the index finger points awayfrom the body; the thumb points toward the ceiling; and the middlefinger points left-right. The three fingers thus describe (roughly, butwith clearly evident intent) the three mutually orthogonal axes of athree-space coordinate system: thus ‘XYZ-hand’.

XYZ-hand navigation then proceeds with the hand, fingers in a pose asdescribed above, held before the operator's body at a predetermined‘neutral location’. Access to the three translational and threerotational degrees of freedom of a three-space object (or camera) iseffected in the following natural way: left-right movement of the hand(with respect to the body's natural coordinate system) results inmovement along the computational context's x-axis; up-down movement ofthe hand results in movement along the controlled context's y-axis; andforward-back hand movement (toward/away from the operator's body)results in z-axis motion within the context. Similarly, rotation of theoperator's hand about the index finger leads to a ‘roll’ change of thecomputational context's orientation; ‘pitch’ and ‘yaw’ changes areeffected analogously, through rotation of the operator's hand about themiddle finger and thumb, respectively.

Note that while ‘computational context’ is used here to refer to theentity being controlled by the XYZ-hand method—and seems to suggesteither a synthetic three-space object or camera—it should be understoodthat the technique is equally useful for controlling the various degreesof freedom of real-world objects: the pan/tilt/roll controls of a videoor motion picture camera equipped with appropriate rotational actuators,for example. Further, the physical degrees of freedom afforded by theXYZ-hand posture may be somewhat less literally mapped even in a virtualdomain: In the present embodiment, the XYZ-hand is also used to providenavigational access to large panoramic display images, so thatleft-right and up-down motions of the operator's hand lead to theexpected left-right or up-down ‘panning’ about the image, butforward-back motion of the operator's hand maps to ‘zooming’ control.

In every case, coupling between the motion of the hand and the inducedcomputational translation/rotation may be either direct (i.e. apositional or rotational offset of the operator's hand maps one-to-one,via some linear or nonlinear function, to a positional or rotationaloffset of the object or camera in the computational context) or indirect(i.e. positional or rotational offset of the operator's hand mapsone-to-one, via some linear or nonlinear function, to a first orhigher-degree derivative of position/orientation in the computationalcontext; ongoing integration then effects a non-static change in thecomputational context's actual zero-order position/orientation). Thislatter means of control is analogous to use of a an automobile's ‘gaspedal’, in which a constant offset of the pedal leads, more or less, toa constant vehicle speed.

The ‘neutral location’ that serves as the real-world XYZ-hand's localsix-degree-of-freedom coordinate origin may be established (1) as anabsolute position and orientation in space (relative, say, to theenclosing room); (2) as a fixed position and orientation relative to theoperator herself (e.g. eight inches in front of the body, ten inchesbelow the chin, and laterally in line with the shoulder plane),irrespective of the overall position and ‘heading’ of the operator; or(3) interactively, through deliberate secondary action of the operator(using, for example, a gestural command enacted by the operator's‘other’ hand, said command indicating that the XYZ-hand's presentposition and orientation should henceforth be used as the translationaland rotational origin).

It is further convenient to provide a ‘detent’ region (or ‘dead zone’)about the XYZ-hand's neutral location, such that movements within thisvolume do not map to movements in the controlled context.

Other poses may included:

[♂||||:vx] is a flat hand (thumb parallel to fingers) with palm facingdown and fingers forward.

[|||||:x̂] is a flat hand with palm facing forward and fingers towardceiling.

[|||||:x] is a flat hand with palm facing toward the center of the body(right if left hand, left if right hand) and fingers forward.

[̂-:-x] is a single-hand thumbs-up (with thumb pointing toward ceiling).

[̂|-:-x] is a mime gun pointing forward.

Two Hand Combination

The present invention contemplates single hand commands and poses, aswell as two-handed commands and poses. FIG. 5 illustrates examples oftwo hand combinations and associated notation in an embodiment of theinvention. Reviewing the notation of the first example, “full stop”reveals that it comprises two closed fists. The “snapshot” example hasthe thumb and index finger of each hand extended, thumbs pointing towardeach other, defining a goal post shaped frame. The “rudder and throttlestart position” is fingers and thumbs pointing up palms facing thescreen.

Orientation Blends

FIG. 6 illustrates an example of an orientation blend in an embodimentof the invention. In the example shown the blend is represented byenclosing pairs of orientation notations in parentheses after the fingerpose string. For example, the first command shows finger positions ofall pointing straight. The first pair of orientation commands wouldresult in the palms being flat toward the display and the second pairhas the hands rotating to a 45 degree pitch toward the screen. Althoughpairs of blends are shown in this example, any number of blends arecontemplated in the invention.

Example Commands

FIG. 8 illustrates a number of possible commands that may be used withthe present invention. Although some of the discussion here has beenabout controlling a cursor on a display, the invention is not limited tothat activity. In fact, the invention has great application inmanipulating any and all data and portions of data on a screen, as wellas the state of the display. For example, the commands may be used totake the place of video controls during play back of video media. Thecommands may be used to pause, fast forward, rewind, and the like. Inaddition, commands may be implemented to zoom in or zoom out of animage, to change the orientation of an image, to pan in any direction,and the like. The invention may also be used in lieu of menu commandssuch as open, close, save, and the like. In other words, any commands oractivity that can be imagined can be implemented with hand gestures.

Operation

FIG. 7 is a flow diagram illustrating the operation of the invention inone embodiment. At step 701 the detection system detects the markers andtags. At decision block 702 it is determined if the tags and markers aredetected. If not, the system returns to step 701. If the tags andmarkers are detected at step 702, the system proceeds to step 703. Atstep 703 the system identifies the hand, fingers and pose from thedetected tags and markers. At steps 704 the system identifies theorientation of the pose. At step 705 the system identifies the threedimensional spatial location of the hand or hands that are detected.(Please note that any or all of steps 703, 704, and 705 may be combinedas a single step).

At step 706 the information is translated to the gesture notationdescribed above. At decision block 707 it is determined if the pose isvalid. This may be accomplished via a simple string comparison using thegenerated notation string. If the pose is not valid, the system returnsto step 701. If the pose is valid, the system sends the notation andposition information to the computer at step 708. At step 709 thecomputer determines the appropriate action to take in response to thegesture and updates the display accordingly at step 710.

In one embodiment of the invention, steps 701-705 are accomplished bythe on-camera processor. In other embodiments, the processing can beaccomplished by the system computer if desired.

Parsing and Translation

The system is able to “parse” and “translate” a stream of low-levelgestures recovered by an underlying system, and turn those parsed andtranslated gestures into a stream of command or event data that can beused to control a broad range of computer applications and systems.These techniques and algorithms may be embodied in a system consistingof computer code that provides both an engine implementing thesetechniques and a platform for building computer applications that makeuse of the engine's capabilities.

One embodiment is focused on enabling rich gestural use of human handsin computer interfaces, but is also able to recognize gestures made byother body parts (including, but not limited to arms, torso, legs andthe head), as well as non-hand physical tools of various kinds, bothstatic and articulating, including but not limited to calipers,compasses, flexible curve approximators, and pointing devices of variousshapes. The markers and tags may be applied to items and tools that maybe carried and used by the operator as desired.

The system described here incorporates a number of innovations that makeit possible to build gestural systems that are rich in the range ofgestures that can be recognized and acted upon, while at the same timeproviding for easy integration into applications.

The gestural parsing and translation system in one embodiment consistsof:

1) a compact and efficient way to specify (encode for use in computerprograms) gestures at several different levels of aggregation:

-   -   a. a single hand's “pose” (the configuration and orientation of        the parts of the hand relative to one another) a single hand's        orientation and position in three-dimensional space.    -   b. two-handed combinations, for either hand taking into account        pose, position or both.    -   c. multi-person combinations; the system can track more than two        hands, and so more than one person can cooperatively (or        competitively, in the case of game applications) control the        target system.    -   d. sequential gestures in which poses are combined in a series;        we call these “animating” gestures.    -   e. “grapheme” gestures, in which the operator traces shapes in        space.

2) a programmatic technique for registering specific gestures from eachcategory above that are relevant to a given application context.

3) algorithms for parsing the gesture stream so that registered gesturescan be identified and events encapsulating those gestures can bedelivered to relevant application contexts.

The specification system (1), with constituent elements (1 a) to (1 f),provides the basis for making use of the gestural parsing andtranslating capabilities of the system described here.

A single-hand “pose” is represented as a string of

i) relative orientations between the fingers and the back of the hand,

ii) quantized into a small number of discrete states.

Using relative joint orientations allows the system described here toavoid problems associated with differing hand sizes and geometries. No“operator calibration” is required with this system. In addition,specifying poses as a string or collection of relative orientationsallows more complex gesture specifications to be easily created bycombining pose representations with further filters and specifications.

Using a small number of discrete states for pose specification makes itpossible to specify poses compactly as well as to ensure accurate poserecognition using a variety of underlying tracking technologies (forexample, passive optical tracking using cameras, active optical trackingusing lighted dots and cameras, electromagnetic field tracking, etc).

Gestures in every category (1 a) to (1 f) may be partially (orminimally) specified, so that non-critical data is ignored. For example,a gesture in which the position of two fingers is definitive, and otherfinger positions are unimportant, may be represented by a singlespecification in which the operative positions of the two relevantfingers is given and, within the same string, “wild cards” or generic“ignore these” indicators are listed for the other fingers.

All of the innovations described here for gesture recognition, includingbut not limited to the multi-layered specification technique, use ofrelative orientations, quantization of data, and allowance for partialor minimal specification at every level, generalize beyond specificationof hand gestures to specification of gestures using other body parts and“manufactured” tools and objects.

The programmatic techniques for “registering gestures” (2), consist of adefined set of Application Programming Interface calls that allow aprogrammer to define which gestures the engine should make available toother parts of the running system.

These API routines may be used at application set-up time, creating astatic interface definition that is used throughout the lifetime of therunning application. They may also be used during the course of the run,allowing the interface characteristics to change on the fly. Thisreal-time alteration of the interface makes it possible to,

i) build complex contextual and conditional control states,

ii) to dynamically add hysterisis to the control environment, and

iii) to create applications in which the user is able to alter or extendthe interface vocabulary of the running system itself.

Algorithms for parsing the gesture stream (3) compare gestures specifiedas in (1) and registered as in (2) against incoming low-level gesturedata. When a match for a registered gesture is recognized, event datarepresenting the matched gesture is delivered up the stack to runningapplications.

Efficient real-time matching is desired in the design of this system,and specified gestures are treated as a tree of possibilities that areprocessed as quickly as possible.

In addition, the primitive comparison operators used internally torecognize specified gestures are also exposed for the applicationsprogrammer to use, so that further comparison (flexible state inspectionin complex or compound gestures, for example) can happen even fromwithin application contexts.

Recognition “locking” semantics are an innovation of the systemdescribed here. These semantics are implied by the registration API (2)(and, to a lesser extent, embedded within the specification vocabulary(1)). Registration API calls include,

i) “entry” state notifiers and “continuation” state notifiers, and

ii) gesture priority specifiers.

If a gesture has been recognized, its “continuation” conditions takeprecedence over all “entry” conditions for gestures of the same or lowerpriorities. This distinction between entry and continuation states addssignificantly to perceived system usability.

The system described here includes algorithms for robust operation inthe face of real-world data error and uncertainty. Data from low-leveltracking systems may be incomplete (for a variety of reasons, includingocclusion of markers in optical tracking, network drop-out or processinglag, etc).

Missing data is marked by the parsing system, and interpolated intoeither “last known” or “most likely” states, depending on the amount andcontext of the missing data.

If data about a particular gesture component (for example, theorientation of a particular joint) is missing, but the “last known”state of that particular component can be analyzed as physicallypossible, the system uses this last known state in its real-timematching.

Conversely, if the last known state is analyzed as physicallyimpossible, the system falls back to a “best guess range” for thecomponent, and uses this synthetic data in its real-time matching.

The specification and parsing systems described here have been carefullydesigned to support “handedness agnosticism,” so that for multi-handgestures either hand is permitted to satisfy pose requirements.

Coincident Virtual/Display and Physical Spaces

The system can provide an environment in which virtual space depicted onone or more display devices (“screens”) is treated as coincident withthe physical space inhabited by the operator or operators of the system.An embodiment of such an environment is described here. This currentembodiment includes three projector-driven screens at fixed locations,is driven by a single desktop computer, and is controlled using thegestural vocabulary and interface system described herein. Note,however, that any number of screens are supported by the techniquesbeing described; that those screens may be mobile (rather than fixed);that the screens may be driven by many independent computerssimultaneously; and that the overall system can be controlled by anyinput device or technique.

The interface system described in this disclosure should have a means ofdetermining the dimensions, orientations and positions of screens inphysical space. Given this information, the system is able todynamically map the physical space in which these screens are located(and which the operators of the system inhabit) as a projection into thevirtual space of computer applications running on the system. As part ofthis automatic mapping, the system also translates the scale, angles,depth, dimensions and other spatial characteristics of the two spaces ina variety of ways, according to the needs of the applications that arehosted by the system.

This continuous translation between physical and virtual space makespossible the consistent and pervasive use of a number of interfacetechniques that are difficult to achieve on existing applicationplatforms or that must be implemented piece-meal for each applicationrunning on existing platforms. These techniques include (but are notlimited to):

1) Use of “literal pointing”—using the hands in a gestural interfaceenvironment, or using physical pointing tools or devices—as a pervasiveand natural interface technique.

2) Automatic compensation for movement or repositioning of screens.

3) Graphics rendering that changes depending on operator position, forexample simulating parallax shifts to enhance depth perception.

4) Inclusion of physical objects in on-screen display--taking intoaccount real-world position, orientation, state, etc. For example, anoperator standing in front of a large, opaque screen, could see bothapplications graphics and a representation of the true position of ascale model that is behind the screen (and is, perhaps, moving orchanging orientation).

It is important to note that literal pointing is different from theabstract pointing used in mouse-based windowing interfaces and mostother contemporary systems. In those systems, the operator must learn tomanage a translation between a virtual pointer and a physical pointingdevice, and must map between the two cognitively.

By contrast, in the systems described in this disclosure, there is nodifference between virtual and physical space (except that virtual spaceis more amenable to mathematical manipulation), either from anapplication or user perspective, so there is no cognitive translationrequired of the operator.

The closest analogy for the literal pointing provided by the embodimentdescribed here is the touch-sensitive screen (as found, for example, onmany ATM machines). A touch-sensitive screen provides a one to onemapping between the two-dimensional display space on the screen and thetwo-dimensional input space of the screen surface. In an analogousfashion, the systems described here provide a flexible mapping(possibly, but not necessarily, one to one) between a virtual spacedisplayed on one or more screens and the physical space inhabited by theoperator. Despite the usefulness of the analogy, it is worthunderstanding that the extension of this “mapping approach” to threedimensions, an arbritrarialy large architectural environment, andmultiple screens is non-trivial.

In addition to the components described herein, the system may alsoimplement algorithms implementing a continuous, systems-level mapping(perhaps modified by rotation, translation, scaling or other geometricaltransformations) between the physical space of the environment and thedisplay space on each screen.

A rendering stack which takes the computational objects and the mappingand outputs a graphical representation of the virtual space.

An input events processing stack which takes event data from a controlsystem (in the current embodiment both gestural and pointing data fromthe system and mouse input) and maps spatial data from input events tocoordinates in virtual space. Translated events are then delivered torunning applications.

A “glue layer” allowing the system to host applications running acrossseveral computers on a local area network.

Gesture-Based Control Using Three-Dimensional Information Extracted Overan Extended Depth of Field

FIG. 9 is a block diagram of gesture-based control system 900 includingan imaging system that extracts three-dimensional information over anextended depth of field, under an embodiment. A user locates his hands101 and 102 in the viewing area of an array of cameras 904A-904D. Atleast two cameras of the array 904A-904D are wavefront coding cameras,each of which comprise elements of a wavefront coding imaging systemincluding wavefront coding masks (also referred to herein as “opticalaspheric element” or “optical element”), as described in detail below.The user's hands and/or fingers may or may not include the marker tagsdescribed above.

The cameras 904A-904D detect or capture images of the fingers and hands101 and 102 including location, orientation, and movement of the fingersand hands 101 and 102 and generate output signals to pre-processor 905.Pre-processor 905 can include or be coupled to the wavefront codingdigital signal processing 908, as described below. Alternatively, thewavefront coding digital signal processing can be included in, coupledto, or distributed among one or more other components of the system 900.The wavefront coding digital signal processing 908 is configured tovastly extend the depth of field of imaging systems.

Pre-processor 905 translates the camera output into a gesture signalthat is provided to the computer processing unit 907 of the system. Inso doing, the pre-processor 905 generates three dimensional space pointreconstruction and skeletal point labeling. The gesture translator 906converts the 3D spatial information and marker motion information into acommand language that can be interpreted by a computer processor toupdate the location, shape, and action of a cursor on a display. Thecomputer 907 uses the input information to generate a command to controlone or more on screen cursors and provides video output to display 903.

One or more of the pre-processor 905, gesture translator 906, andcomputer 907 of an alternative embodiment can be combined into a singledevice. Regardless of system configuration, the functions and/orfunctionality of each of the pre-processor 905, gesture translator 906,and computer 907 are as described above with reference to FIGS. 1-8 andelsewhere herein.

Furthermore, while this example shows four cameras being used to detectthe location, orientation, and movement of the user's hands 101 and 102,the embodiment is not so limited. The system configuration can includetwo or more cameras as appropriate to a system or workstationconfiguration. In addition, although the cameras are disposedsymmetrically in the example embodiment, there is no requirement of suchsymmetry. Thus, at least two cameras with any positioning that permitsthe location, orientation, and movement of the user's hands may be usedhereunder.

Although the system is shown with a single user's hands as input, thesystem can track hands of any number of multiple users. In addition,instead of or in addition to hands, the system may track any part orparts of a user's body, including head, feet, legs, arms, elbows, knees,and the like. Furthermore, the system can track any number of animate orinanimate objects and is not limited to tracking parts of a body.

In particular, for gesture analysis systems that locate an opticalsensor so as to be deliberately or potentially proximal to an operator'shand (or equivalently tracked implement), the elements thus apprehendedwill typically range, throughout a natural sequence of operator motion,over several or many orders of relative distance magnitude. It is beyondthe capacity of traditional optical imaging systems to provide aconsistently focus-resolved record of events traversing such a range ofdistances. These close-approach to medium-distance geometries are oftenhowever desirable in the context of object- or operator-tracking for thepurposes of macroscopic device and product design. It is thus of valueto provide a technique (for which purpose traditional optics isinadequate) for insuring local contrast or salient feature stabilityover the expected range of operator activity.

In describing the extraction of three-dimensional information over anextended depth of field as used in the systems herein, distance to apoint in a scene can be estimated from its location in two or moreimages captured simultaneously. The three dimensional (3D) position ofthe point can be computed from basic geometric relationships when the 3Drelationship between the imagers is known. The challenge in computingspatial location from multiple images, often referred to as stereocorrelation or stereo depth computation, is automatically and accuratelyassociating the mapping of a point in one image with its mapping inanother image. This is most often done by correlating image featuresfrom one image to one or more others. The underlying assumption in allstereo matching methods, however, is that there must be someidentifiable local contrast or feature in the image in order to matchthat point to its location in another image. Therefore a problem ariseswhen there is no local contrast or feature in the image because ofmisfocus—stereo matching does not produce accurate results in regions ofan image that are out of focus.

The conventional means for extending the focal depth of an image is toreduce the diameter of the camera's lens's pupil (“stopping down”).However, two side effects restrict the usefulness of the technique.First, the sensitivity of the imaging system is reduced by a factorequal to the square of the pupil diameter ratio. Second, the maximumspatial frequency response is reduced a factor equal to the pupildiameter ratio, which limits the resolution and contrast in the image.There is thus a tradeoff between depth of field, exposure time, andoverall contrast in conventional imaging systems. In the case of amultiple camera ranging system, the net effect will be a compromisebetween stereoscopic depth accuracy and working range.

An alternate approach to increasing depth of field without stopping thelens is to introduce a phase mask of specified prescription in the pupilof the camera lens. With a properly chosen phase function, an extendeddepth of field can be recovered by subsequent electronic processing ofthe image captured on the sensor. This technique, known as wavefrontcoding, generally provides a tradeoff among depth of field, cameradynamic range, and signal-to-noise ratio. Wavefront coding makes itpossible to optimize the camera parameters for a specific application.Applications that do not require a very high dynamic range and in whichthe illumination is under user control, such as gesture recognitiondescribed herein, can greatly benefit from wavefront coding to achieve ahigh accuracy over a prescribed volume of space.

As described above, the system of an embodiment includes a technique inwhich the processed outputs of a plurality of wavefront coding camerasare used to determine the range and position of selected objects withina scene. The extended depth of field that results from wavefront codingcan be used in a number of applications, including gesture recognitionand a broad array of other task-based imaging work, to significantlyincrease their performance. Although a minimum of two cameras isrequired, there is no upper limit to the number of cameras that can beused in the embodiment. The scene extraction can include any of aplurality of processing techniques (such as correlations) that are usedfor range extraction with two or more cameras. The embodiments describedherein include all wavefront coding phase functions, and theircorresponding decoding kernels, that result in an extended depth offield after processing.

Wavefront coding, as used in wavefront coding imaging systems, is ageneral technique of using generalized aspheric optics and digitalsignal processing to greatly increase the performance and/or reduce thecost of imaging systems. The type of aspheric optics employed results inoptical imaging characteristics that are very insensitive to misfocusrelated aberrations. A sharp and clear image is not directly producedfrom the optics, however, digital signal processing applied to thesampled image produces a sharp and clear final image that is alsoinsensitive to misfocus related aberrations.

Wavefront coding is used to greatly increase imaging performance whilealso reducing the size, weight, and cost of imaging systems. Wavefrontcoding combines non-rotationally symmetric aspheric optical elements anddigital signal processing in a fundamental manner to vastly extend thedepth of field of imaging systems. With wavefront coding the depth offield or depth of focus of an imaging system can be increased by afactor of ten or more compared to traditional imaging systems, for agiven aperture size or F/#, for example. Wavefront coding opticalelements of an embodiment are phase surfaces and as such do not absorblight or increase exposure or illumination requirements. Such extendeddepth of field performance is impossible with traditional imagingtechniques without dramatic loss of optical power, such as required withstopped down apertures. Increased depth of field/depth of focus alsoenables imaging systems to be physically less expensive, smaller, orlighter by controlling misfocus related aberrations that aretraditionally controlled by adding lens elements or increasing lenscomplexity. Misfocus related aberrations that can be controlled withwavefront coding include chromatic aberration, Petzval curvature,astigmatism, spherical aberration, and temperature related misfocus.

Wavefront coding, as a hybrid imaging approach, combines optics andelectronics to increase depth of field and reduce the number of opticalelements, fabrication tolerances, and overall system cost. FIG. 10 is ablock diagram of a wavefront coding imaging system 1000 used in agesture-based control system, under an embodiment. The optical section1001 of the wavefront coding imaging system 1000 is a conventionaloptical system or camera modified with a wavefront coding opticalelement 1002 placed near the aperture stop. The addition of the codingoptical element results in images with a specialized well-defined bluror point spread function that is insensitive to misfocus. Digitalprocessing 1003 applied to the sampled image produces a sharp and clearimage 1004 that is very insensitive to misfocus effects.

FIG. 11 is a block diagram of gesture-based control system 1100 forextracting three-dimensional information over an extended depth of fieldusing a wavefront coding imaging system that includes two wavefrontcoding cameras, under an embodiment. The system 1100 includes at leasttwo wavefront coding cameras 1101 and 1102, as described above withreference to FIG. 10. A processor is coupled to receive the output ofthe wavefront coding cameras 1101 and 1102 and to perform dataprocessing on the camera output. The data processing includesdeconvolution 1120 and range extraction 1130, to name a few, andgenerate an extended focus range map 1140.

In the wavefront coding system 1100, the optical portion of the system(e.g., wavefront coding cameras 1101 and 1102) “codes” the resultingimages to produce intermediate images 1110. Because the wavefront codingelement (e.g., FIG. 10, element 1002) purposefully blurs all points inany image, the intermediate image 1110 appears misfocused. In suchintermediate images 1110, nearly all the objects within the field ofview are blurred, but they are blurred identically. In contrast,traditional optics typically form images that have a variable blurfunction that is dependent on the distance to each object in the scene.

In order to produce a sharp and clear image from the intermediatewavefront-coded image 1110, electronics (e.g., wavefront coding digitalsignal processing) are used to process or “decode” 1120 and 1130 theblurred intermediate image by removing the system-dependent image blur.The digital filtering can be performed in real-time by software or withspecialized hardware solutions.

The system optics of an embodiment include conventional components withat least one additional optical element that performs the wavefrontcoding function, as described above with reference to FIG. 10. Thiselement is placed in the optical path, typically near an aperture stopof the system to minimize vignetting. The signal processing performed onthe detected image depends on the optics, wavefront coding element, andthe first-order properties of the digital detector.

The general wavefront coding element is nonrotationally symmetric andsmooth, although diffractive surfaces can be used. The element can be aseparate component, or it can be integrated onto a traditional lenselement by the addition of a generalized aspheric surface. All codingelements redirect light so that no ray, besides the on-axis ray, travelstoward the traditional geometric focus point. In fact, no two rays aretraveling toward the same point along the optical axis. The system doesnot form a clear image at any image plane.

The main effect of the optics portion of a wavefront-coded imagingsystem is to make the resulting images insensitive to focus-relatedaberrations such as defocus, spherical aberration, astigmatism, or fieldcurvature. The intermediate blurry image is insensitive or invariant tochanges in the object or imaging system that consist of defocusaberrations. From a systems analysis point of view, the modulationtransfer functions (MTFs) and point spread functions (PSFs) ofwavefront-coded systems are invariant with respect to defocus.

Although the MTF of an intermediate image from a wavefront-coded systemshows little change with defocus, such MTFs do have reduced powercompared with the in-focus traditional system. Since apodization is notused, total optical power is preserved. A digital filtering or imagereconstruction process is used to form a clear image. These final MTFsare very insensitive to defocus—thus, the wavefront-coded imaging systemhas a very large depth of field. Similarly, the intermediate PSFs fromthe wavefront-coded system are different from traditional system PSFs,but they change very little with changes in misfocus.

Referring again to FIG. 10, a special purpose optical aspheric elementis placed at or near the aperture stop of a conventional imaging systemto form a wavefront coding imaging system. This optical element modifiesthe imaging system in such a way that the resulting PSF and opticaltransfer function (OTF) are insensitive to a range of misfocus ormisfocus-related aberrations. The PSF and OTF are not, however, the sameas that obtained with a good quality in-focus imaging system. Theprocess of making the imaging system insensitive to misfocus aberrationsproduces images with a specialized, well defined blur; this blur isremoved with the wavefront coding digital signal processing.

The PSFs from a conventional imaging system, for example, changedrastically with misfocus, while the PSFs from the wavefront codingimaging system show almost no noticeable change with misfocus. Digitalprocessing to remove the misfocus blur applied to a misfocusedtraditional imaging system uses processing dependent on the amount ofmisfocus present in different areas of the image. In many situations theamount of misfocus is unknown and difficult to calculate. In addition,the MTF of the misfocused traditional imaging system can often containzeros or nulls that further increase the difficulty of the digitalprocessing. In contrast, the constant nature of PSFs with misfocus fromthe wavefront coding system is what is needed to eliminate thedependencies of digital processing on misfocus. Digital processingapplied to the charge-coupled device (CCD) or complementarymetal-oxide-semiconductor (CMOS)—detected image is independent ofmisfocus and the actual scene being imaged. In addition, the MTF ofwavefront coding imaging systems, both in and out of focus, contain nozeros or nulls allowing high quality final images.

Wavefront coding for extending the depth of field can add value toimaging applications where traditional methodologies (i.e. stopping downthe aperture) are generally unacceptable. Constraints on illuminationlevels, exposure times, or spatial resolution often limit theapplication of previous optical methods. By using wavefront coding,applications can enjoy fewer misfocus-related problems, withoutsacrificing exposure times or requiring vast quantities of illuminationWavefront coding imaging systems comprise non-conventional opticaldesigns and digital signal processing of the resulting images, asdescribed above. The signal processing used is dependent on the specificoptical system. The wavefront coding optics are dependent on the typeand amount of signal processing to be used. Since the optics and signalprocessing are closely coupled, it is natural to expect best performancefrom systems where the optical and digital components of the system arejointly optimized during design. The optical components are configuredto minimize the changes or sensitivity of the optics to misfocus effectsas well enable efficient signal processing. The digital components aredesigned to minimize algorithm complexity, processing time, and effectsof digital processing on image noise.

FIG. 12 is a flow diagram for gesture-based control usingthree-dimensional information extracted over an extended depth of field,under an embodiment. The gesture-based control of an embodimentcomprises imaging 1202 a body with an imaging system. The imaging 1202comprises generating wavefront coded images of the body. Thegesture-based control of an embodiment comprises automatically detecting1204 a gesture of a body, the gesture including an instantaneous stateof the body. The detecting 1204 includes aggregating gesture data of thegesture at an instant in time. The gesture data comprises focus-resolveddata of the body within a depth of field of the imaging system. Thegesture-based control of an embodiment comprises translating 1206 thegesture to a gesture signal. The gesture-based control of an embodimentcomprises controlling 1208 a component coupled to a computer in responseto the gesture signal.

The base routine for wavefront coding of an embodiment can include aray-trace program that traces rays through typical spherical andaspherical surfaces as well as general wavefront coding surface forms.The ray-trace program is used to calculate exit pupils and optimize agiven set of optical and digital merit functions or operands. FIG. 13 isa block diagram of a wavefront coding design process 1300 used in agesture-based control system, under an embodiment. The output of thisdesign includes but is not limited to the following: traditional opticalsurfaces, materials, thickness, and spacings; parameters of wavefrontcoding surfaces; and, digital filter coefficients.

The general optical/digital design loop is now described with referenceto FIG. 13. A ray-trace program 1302 traces rays through opticalsurfaces to calculate exit pupil optical path differences (OPDs) 1304and optimize a given set of optical and digital merit functions oroperands. Inputs to the ray-trace program 1302 include optical surfaces,thickness, and operating conditions (wavelengths, field of view,temperature range, sample object images, etc.) to name a few. The OTFsare calculated or generated 1306, and pixel OTFs related to detectorgeometry are added 1308. Sampled OTFs and PSFs are calculated 1310.Digital filter coefficients are generated 1312 for a selected processingalgorithm based on the sampled PSFs. The processing continues by formingfigures of merit (e.g., wavefront coding operands) for the filter thatare based on minimizing: changes of the sampled PSF and MTF throughfocus, with field angle, with color, due to temperature changes, due toaliasing, etc.; digital processing parameters such as amount ofprocessing, form of the processing, processing related image noise,digital filter noise gain etc. The wavefront coding operands arecombined with traditional optical operands (Seidel Wavefrontaberrations, RMS Wavefront errors, etc.) through optimization routinesto modify the optical surfaces. Operations return to generate 1302 exitpupil optical path differences (OPDs) via traditional ray tracing.

Theoretically calculated wavefront coding surface forms are used asstarting points for the optical optimization. One general family ofrectangularly separable surface forms is given in normalized coordinatesas:

S(x)=|β| sign(x) |x|^(α)

where sign(x)=+1 for x >0,and sign(x)=−1 for x ≦0.

The exponential parameter a controls the height of the MTF over a rangeof misfocus, and the parameter β controls the sensitivity to misfocus.In general, increasing the parameter β decreases the sensitivity tomisfocus while decreasing the height of the MTF and increasing thelength of the resulting PSF.

The filtering process used to reconstruct the intermediate images andproduce final images can impose a computational burden. The size of thefilter kernel required for image reconstruction may be as large as 70×70coefficients, depending on the optical system and the enhancement todepth-of-field introduced by the coding process. Generally, the largerthe depth of field extension, the larger the filter kernel, and thelarger the noise penalty or noise gain. Furthermore, because every pixelin an image is blurred by wavefront coding, every pixel needs to befiltered; thus, larger images can require more computation than smallerimages. With image sizes approaching tens of millions of pixels,efficient computational solutions are used for practical and economicalsystems. Computational implementations, such as rectangularly separablefilter approximations, can help reduce kernel dimensions. The wavefrontcoding element used, for example, can have a rectangularly separablecubic phase form described as

S(x,y)=α(x³+y³).

Filtering a blurred image to remove the blur essentially imposes anamplification and phase shift as a function of spatial frequency. Thisamplification increases the signal as well as the noise in the finalimages. For very large depth-of-field enhancements, for instance, over10 times, the noise gain in a wavefront-coded system can be a factor offour or five. For more moderate depth-of-field enhancements of two tofour, the noise gain is typically a factor of two or less.

For uncorrelated Gaussian noise (a good assumption for most images) thenoise gain is the RMS value of the filter coefficients. For systems withdepth-of-field extensions too large to produce a suitably smallnoise-gain value, reducing the resolution or spatial bandwidth of thedigital filter can reduce the noise gain. Reducing the contrast in thefinal image also can reduce the overall effects of the increased noise.Specialized nonlinear filtering is the best solution for removing noisein wavefront-coded images.

Because the wavefront coding optical element used to form the MTFs andPSFs in an embodiment is rectangularly separable, the signal processingused may also be rectangularly separable. Rectangularly separableprocessing can reduce the required number of computations by an order ofmagnitude or more. Due to the fact that the digital filtering isperformed with spatial convolution, the computational methods of anembodiment comprise a series of multiplications to scale the data by thefilter coefficients and summations to add all the scaled data valuestogether across the entire kernel. The fundamental unit of such acomputation is a multiply-accumulate operation. A typical 2-D wavefrontcoding filter kernel for a large depth-of-field increase might be 30×30coefficients. A rectangularly separable version of this filter wouldcontain a row filter that is 30 coefficients long and a column filterthat is 30 coefficients tall, or 60 total coefficients. While wavefrontcoding elements can be rectangulalry separable in design, they are notso limited, and highly aberrated systems may use nonseparable filtering.

By combining optical imaging techniques with electronic filtering,wavefront coding technology can improve performance for a wide range ofimaging systems. The performance gains in high-performance imagingsystems can include very large depth-of-field without sacrificing lightgathering or spatial resolution. The performance gains in lower-costimaging systems can include good image quality with fewer physicalcomponents than traditionally required.

The embodiments described herein include a system comprising: aplurality of optical detectors, wherein at least two optical detectorsof the plurality of optical detectors comprise wavefront coding or codedcameras, wherein the plurality of optical detectors image a body; and aprocessor coupled to the plurality of optical detectors, the processorautomatically detecting a gesture of a body, wherein the gesturecomprises an instantaneous state of the body, wherein the detectingcomprises aggregating gesture data of the gesture at an instant in time,the gesture data comprising focus-resolved data of the body within adepth of field of the imaging system, the processor translating thegesture to a gesture signal and using the gesture signal to control acomponent coupled to the processor.

The wavefront coding cameras of an embodiment include a wavefront codingoptical element.

The imaging of an embodiment comprises generating wavefront coded imagesof the body.

The wavefront coding cameras of an embodiment include a phase mask thatincreases a depth of focus of the imaging.

The gesture data of an embodiment comprises focus-resolved range data ofthe body within the depth of field.

The focus-resolved range data of the body within the depth of field ofan embodiment is derived from an output of the wavefront coding cameras.

The gesture data of an embodiment comprises focus-resolved position dataof the body within the depth of field.

The focus-resolved position data of the body within the depth of fieldof an embodiment is derived from an output of the wavefront codingcameras.

The system of an embodiment comprises modulation transfer functions andpoint spread functions that are invariant to a distance between the bodyand the imaging system.

The system of an embodiment comprises modulation transfer functions andpoint spread functions that are invariant with respect to defocus.

The processor of an embodiment generates intermediate images by codingimages gathered by the wavefront coding cameras.

The intermediate images of an embodiment are blurred.

The intermediate images of an embodiment are insensitive to changes inthe body or the plurality of optical detectors that include defocusaberrations.

The gesture data of an embodiment is three-space location datarepresenting the gesture.

The detecting of an embodiment includes at least one of detecting alocation of the body, detecting an orientation of the body, anddetecting includes detecting motion of the body.

The detecting of an embodiment comprises identifying the gesture,wherein the identifying includes identifying a pose and an orientationof a portion of the body.

The detecting of an embodiment includes detecting at least one of afirst set of appendages and a second set of appendages of the body.

The detecting of an embodiment includes dynamically detecting a positionof at least one tag.

The detecting of an embodiment includes detecting position of a set oftags coupled to a part of the body.

Each tag of the set of tags of an embodiment includes a pattern, whereineach pattern of each tag of the set of tags is different than anypattern of any remaining tag of the plurality of tags.

The detecting of an embodiment includes dynamically detecting andlocating a marker on the body.

The detecting of an embodiment includes detecting position of a set ofmarkers coupled to a part of the body.

The set of markers of an embodiment form a plurality of patterns on thebody.

The detecting of an embodiment includes detecting position of aplurality of appendages of the body using a set of markers coupled toeach of the appendages.

The translating of an embodiment comprises translating information ofthe gesture to a gesture notation.

The gesture notation of an embodiment represents a gesture vocabulary,and the gesture signal comprises communications of the gesturevocabulary.

The gesture vocabulary of an embodiment represents in textual forminstantaneous pose states of kinematic linkages of the body.

The gesture vocabulary of an embodiment represents in textual form anorientation of kinematic linkages of the body.

The gesture vocabulary of an embodiment represents in textual form acombination of orientations of kinematic linkages of the body.

The gesture vocabulary of an embodiment includes a string of charactersthat represent a state of kinematic linkages of the body.

The kinematic linkage of an embodiment is at least one first appendageof the body.

The system of an embodiment comprises assigning each position in thestring to a second appendage, the second appendage connected to thefirst appendage.

The system of an embodiment comprises assigning characters of aplurality of characters to each of a plurality of positions of thesecond appendage.

The plurality of positions of an embodiment is established relative to acoordinate origin.

The system of an embodiment comprises establishing the coordinate originusing a position selected from a group consisting of an absoluteposition and orientation in space, a fixed position and orientationrelative to the body irrespective of an overall position and heading ofthe body, and interactively in response to an action of the body.

The system of an embodiment comprises assigning characters of theplurality of characters to each of a plurality of orientations of thefirst appendage.

The detecting of an embodiment comprises detecting when an extrapolatedposition of the body intersects virtual space, wherein the virtual spacecomprises space depicted on a display device coupled to the computer.

Controlling the component of an embodiment comprises controlling avirtual object in the virtual space when the extrapolated positionintersects the virtual object.

Controlling the component of an embodiment comprises controlling aposition of the virtual object in the virtual space in response to theextrapolated position in the virtual space.

Controlling the component of an embodiment comprises controllingattitude of the virtual object in the virtual space in response to thegesture.

The system of an embodiment comprises controlling scaling of thedetecting and controlling to generate coincidence between virtual spaceand physical space, wherein the virtual space comprises space depictedon a display device coupled to the processor, wherein the physical spacecomprises space inhabited by the body.

The system of an embodiment comprises controlling at least one virtualobject in the virtual space in response to movement of at least onephysical object in the physical space.

The controlling of an embodiment includes at least one of controlling afunction of an application hosted on the processor and controlling acomponent displayed on the processor.

The embodiments described herein include a method comprising: imaging abody with an imaging system, the imaging comprising generating wavefrontcoded images of the body; automatically detecting a gesture of a body,wherein the gesture comprises an instantaneous state of the body,wherein the detecting comprises aggregating gesture data of the gestureat an instant in time, the gesture data comprising focus-resolved dataof the body within a depth of field of the imaging system; translatingthe gesture to a gesture signal; and controlling a component coupled toa computer in response to the gesture signal.

The imaging system of an embodiment comprises a plurality of opticaldetectors, wherein at least two of the optical detectors are wavefrontcoding cameras comprising a wavefront coding optical element.

The imaging of an embodiment comprises generating wavefront coded imagesof the body.

The imaging system of an embodiment comprises a plurality of opticaldetectors, wherein at least two of the optical detectors are wavefrontcoding cameras comprising a phase mask that increases a depth of focusof the imaging.

The gesture data of an embodiment comprises focus-resolved range data ofthe body within the depth of field.

The focus-resolved range data of the body within the depth of field ofan embodiment is derived from an output of the imaging system.

The gesture data of an embodiment comprises focus-resolved position dataof the body within the depth of field.

The focus-resolved position data of the body within the depth of fieldof an embodiment is derived from an output of the imaging system.

The method of an embodiment comprises generating modulation transferfunctions and point spread functions that are invariant to a distancebetween the body and the imaging system.

The method of an embodiment comprises generating modulation transferfunctions and point spread functions that are invariant with respect todefocus.

The method of an embodiment comprises generating intermediate images bycoding images gathered by the wavefront coding cameras.

The intermediate images of an embodiment are blurred.

The intermediate images of an embodiment are insensitive to changes inthe body or a plurality of optical detectors of the imaging system thatinclude defocus aberrations.

The gesture data of an embodiment is three-space location datarepresenting the gesture.

The detecting of an embodiment includes detecting a location of thebody.

The detecting of an embodiment includes detecting an orientation of thebody.

The detecting of an embodiment includes detecting motion of the body.

The detecting of an embodiment comprises identifying the gesture,wherein the identifying includes identifying a pose and an orientationof a portion of the body.

The detecting of an embodiment includes detecting at least one of afirst set of appendages and a second set of appendages of the body.

The detecting of an embodiment includes dynamically detecting a positionof at least one tag.

The detecting of an embodiment includes detecting position of a set oftags coupled to a part of the body.

Each tag of the set of tags of an embodiment includes a pattern, whereineach pattern of each tag of the set of tags is different than anypattern of any remaining tag of the plurality of tags.

The detecting of an embodiment includes dynamically detecting andlocating a marker on the body.

The detecting of an embodiment includes detecting position of a set ofmarkers coupled to a part of the body.

The set of markers of an embodiment form a plurality of patterns on thebody.

The detecting of an embodiment includes detecting position of aplurality of appendages of the body using a set of markers coupled toeach of the appendages.

The translating of an embodiment comprises translating information ofthe gesture to a gesture notation.

The gesture notation of an embodiment represents a gesture vocabulary,and the gesture signal comprises communications of the gesturevocabulary.

The gesture vocabulary of an embodiment represents in textual forminstantaneous pose states of kinematic linkages of the body.

The gesture vocabulary of an embodiment represents in textual form anorientation of kinematic linkages of the body.

The gesture vocabulary of an embodiment represents in textual form acombination of orientations of kinematic linkages of the body.

The gesture vocabulary of an embodiment includes a string of charactersthat represent a state of kinematic linkages of the body.

The kinematic linkage of an embodiment is at least one first appendageof the body.

The method of an embodiment comprises assigning each position in thestring to a second appendage, the second appendage connected to thefirst appendage.

The method of an embodiment comprises assigning characters of aplurality of characters to each of a plurality of positions of thesecond appendage.

The plurality of positions of an embodiment is established relative to acoordinate origin.

The method of an embodiment comprises establishing the coordinate originusing a position selected from a group consisting of an absoluteposition and orientation in space, a fixed position and orientationrelative to the body irrespective of an overall position and heading ofthe body, and interactively in response to an action of the body.

The method of an embodiment comprises assigning characters of theplurality of characters to each of a plurality of orientations of thefirst appendage.

The detecting of an embodiment comprises detecting when an extrapolatedposition of the body intersects virtual space, wherein the virtual spacecomprises space depicted on a display device coupled to the computer.

Controlling the component of an embodiment comprises controlling avirtual object in the virtual space when the extrapolated positionintersects the virtual object.

Controlling the component of an embodiment comprises controlling aposition of the virtual object in the virtual space in response to theextrapolated position in the virtual space.

Controlling the component of an embodiment comprises controllingattitude of the virtual object in the virtual space in response to thegesture.

The method of an embodiment comprises controlling scaling of thedetecting and controlling to generate coincidence between virtual spaceand physical space, wherein the virtual space comprises space depictedon a display device coupled to the processor, wherein the physical spacecomprises space inhabited by the body.

The method of an embodiment comprises translating scale, angle, depth,and dimension between the virtual space and the physical space asappropriate to at least one application coupled to the processor.

The method of an embodiment comprises controlling at least one virtualobject in the virtual space in response to movement of at least onephysical object in the physical space.

The controlling of an embodiment includes controlling a function of anapplication hosted on the processor.

The controlling of an embodiment includes controlling a componentdisplayed on the processor.

The systems and methods described herein include and/or run under and/orin association with a processing system. The processing system includesany collection of processor-based devices or computing devices operatingtogether, or components of processing systems or devices, as is known inthe art. For example, the processing system can include one or more of aportable computer, portable communication device operating in acommunication network, and/or a network server. The portable computercan be any of a number and/or combination of devices selected from amongpersonal computers, cellular telephones, personal digital assistants,portable computing devices, and portable communication devices, but isnot so limited. The processing system can include components within alarger computer system.

The processing system of an embodiment includes at least one processorand at least one memory device or subsystem. The processing system canalso include or be coupled to at least one database. The term“processor” as generally used herein refers to any logic processingunit, such as one or more central processing units (CPUs), digitalsignal processors (DSPs), application-specific integrated circuits(ASIC), etc. The processor and memory can be monolithically integratedonto a single chip, distributed among a number of chips or components ofa host system, and/or provided by some combination of algorithms. Themethods described herein can be implemented in one or more of softwarealgorithm(s), programs, firmware, hardware, components, circuitry, inany combination.

System components embodying the systems and methods described herein canbe located together or in separate locations. Consequently, systemcomponents embodying the systems and methods described herein can becomponents of a single system, multiple systems, and/or geographicallyseparate systems. These components can also be subcomponents orsubsystems of a single system, multiple systems, and/or geographicallyseparate systems. These components can be coupled to one or more othercomponents of a host system or a system coupled to the host system.

Communication paths couple the system components and include any mediumfor communicating or transferring files among the components. Thecommunication paths include wireless connections, wired connections, andhybrid wireless/wired connections. The communication paths also includecouplings or connections to networks including local area networks(LANs), metropolitan area networks (MANs), wide area networks (WANs),proprietary networks, interoffice or backend networks, and the Internet.Furthermore, the communication paths include removable fixed mediumslike floppy disks, hard disk drives, and CD-ROM disks, as well as flashRAM, Universal Serial Bus (USB) connections, RS-232 connections,telephone lines, buses, and electronic mail messages.

Unless the context clearly requires otherwise, throughout thedescription, the words “comprise,” “comprising,” and the like are to beconstrued in an inclusive sense as opposed to an exclusive or exhaustivesense; that is to say, in a sense of “including, but not limited to.”Words using the singular or plural number also include the plural orsingular number respectively. Additionally, the words “herein,”“hereunder,” “above,” “below,” and words of similar import refer to thisapplication as a whole and not to any particular portions of thisapplication. When the word “or” is used in reference to a list of two ormore items, that word covers all of the following interpretations of theword: any of the items in the list, all of the items in the list and anycombination of the items in the list.

The above description of embodiments of the processing environment isnot intended to be exhaustive or to limit the systems and methodsdescribed to the precise form disclosed. While specific embodiments of,and examples for, the processing environment are described herein forillustrative purposes, various equivalent modifications are possiblewithin the scope of other systems and methods, as those skilled in therelevant art will recognize. The teachings of the processing environmentprovided herein can be applied to other processing systems and methods,not only for the systems and methods described above.

The elements and acts of the various embodiments described above can becombined to provide further embodiments. These and other changes can bemade to the processing environment in light of the above detaileddescription.

1. A system comprising: a plurality of optical detectors, wherein atleast two optical detectors of the plurality of optical detectorscomprise wavefront coded cameras, wherein the plurality of opticaldetectors image a body; and a processor coupled to the plurality ofoptical detectors, the processor automatically detecting a gesture of abody, wherein the gesture comprises an instantaneous state of the body,wherein the detecting comprises aggregating gesture data of the gestureat an instant in time, the gesture data comprising focus-resolved dataof the body within a depth of field of the imaging system, the processortranslating the gesture to a gesture signal and using the gesture signalto control a component coupled to the processor.
 2. The system of claim1, wherein the wavefront coded cameras include a wavefront codingoptical element.
 3. The system of claim 1, wherein the imaging comprisesgenerating wavefront coded images of the body.
 4. The system of claim 1,wherein the wavefront coded cameras include a phase mask that increasesa depth of focus of the imaging.
 5. The system of claim 1, wherein thegesture data comprises focus-resolved range data of the body within thedepth of field.
 6. The system of claim 5, wherein the focus-resolvedrange data of the body within the depth of field is derived from anoutput of the wavefront coded cameras.
 7. The system of claim 1, whereinthe gesture data comprises focus-resolved position data of the bodywithin the depth of field.
 8. The system of claim 7, wherein thefocus-resolved position data of the body within the depth of field isderived from an output of the wavefront coded cameras.
 9. The system ofclaim 1, comprising modulation transfer functions and point spreadfunctions that are invariant to a distance between the body and theimaging system.
 10. The system of claim 1, comprising modulationtransfer functions and point spread functions that are invariant withrespect to defocus.
 11. The system of claim 1, wherein the processorgenerates intermediate images by coding images gathered by the wavefrontcoded cameras.
 12. The system of claim 11, wherein the intermediateimages are blurred.
 13. The system of claim 11, wherein the intermediateimages are insensitive to changes in the body or the plurality ofoptical detectors that include defocus aberrations.
 14. The system ofclaim 1, wherein the gesture data is three-space location datarepresenting the gesture.
 15. The system of claim 1, wherein thedetecting includes at least one of detecting a location of the body,detecting an orientation of the body, and detecting includes detectingmotion of the body.
 16. The system of claim 1, wherein the detectingcomprises identifying the gesture, wherein the identifying includesidentifying a pose and an orientation of a portion of the body.
 17. Thesystem of claim 1, wherein the detecting includes detecting at least oneof a first set of appendages and a second set of appendages of the body.18. The system of claim 1, wherein the detecting includes dynamicallydetecting a position of at least one tag.
 19. The system of claim 18,wherein the detecting includes detecting position of a set of tagscoupled to a part of the body.
 20. The system of claim 19, wherein eachtag of the set of tags includes a pattern, wherein each pattern of eachtag of the set of tags is different than any pattern of any remainingtag of the plurality of tags.
 21. The system of claim 1, wherein thedetecting includes dynamically detecting and locating a marker on thebody.
 22. The system of claim 21, wherein the detecting includesdetecting position of a set of markers coupled to a part of the body.23. The system of claim 21, wherein the set of markers form a pluralityof patterns on the body.
 24. The system of claim 21, wherein thedetecting includes detecting position of a plurality of appendages ofthe body using a set of markers coupled to each of the appendages. 25.The system of claim 1, wherein the translating comprises translatinginformation of the gesture to a gesture notation.
 26. The system ofclaim 25, wherein the gesture notation represents a gesture vocabulary,and the gesture signal comprises communications of the gesturevocabulary.
 27. The system of claim 26, wherein the gesture vocabularyrepresents in textual form instantaneous pose states of kinematiclinkages of the body.
 28. The system of claim 26, wherein the gesturevocabulary represents in textual form an orientation of kinematiclinkages of the body.
 29. The system of claim 26, wherein the gesturevocabulary represents in textual form a combination of orientations ofkinematic linkages of the body.
 30. The system of claim 26, wherein thegesture vocabulary includes a string of characters that represent astate of kinematic linkages of the body.
 31. The system of claim 30,wherein the kinematic linkage is at least one first appendage of thebody.
 32. The system of claim 31, comprising assigning each position inthe string to a second appendage, the second appendage connected to thefirst appendage.
 33. The system of claim 32, comprising assigningcharacters of a plurality of characters to each of a plurality ofpositions of the second appendage.
 34. The system of claim 33, whereinthe plurality of positions is established relative to a coordinateorigin.
 35. The system of claim 34, comprising establishing thecoordinate origin using a position selected from a group consisting ofan absolute position and orientation in space, a fixed position andorientation relative to the body irrespective of an overall position andheading of the body, and interactively in response to an action of thebody.
 36. The system of claim 33, comprising assigning characters of theplurality of characters to each of a plurality of orientations of thefirst appendage.
 37. The system of claim 31, wherein the detectingcomprises detecting when an extrapolated position of the body intersectsvirtual space, wherein the virtual space comprises space depicted on adisplay device coupled to the computer.
 38. The system of claim 37,wherein controlling the component comprises controlling a virtual objectin the virtual space when the extrapolated position intersects thevirtual object.
 39. The system of claim 38, wherein controlling thecomponent comprises controlling a position of the virtual object in thevirtual space in response to the extrapolated position in the virtualspace.
 40. The system of claim 38, wherein controlling the componentcomprises controlling attitude of the virtual object in the virtualspace in response to the gesture.
 41. The system of claim 1, comprisingcontrolling scaling of the detecting and controlling to generatecoincidence between virtual space and physical space, wherein thevirtual space comprises space depicted on a display device coupled tothe processor, wherein the physical space comprises space inhabited bythe body.
 42. The system of claim 41, comprising controlling at leastone virtual object in the virtual space in response to movement of atleast one physical object in the physical space.
 43. The system of claim1, wherein the controlling includes at least one of controlling afunction of an application hosted on the processor and controlling acomponent displayed on the processor.
 44. A method comprising: imaging abody with an imaging system, the imaging comprising generating wavefrontcoded images of the body; automatically detecting a gesture of a body,wherein the gesture comprises an instantaneous state of the body,wherein the detecting comprises aggregating gesture data of the gestureat an instant in time, the gesture data comprising focus-resolved dataof the body within a depth of field of the imaging system; translatingthe gesture to a gesture signal; and controlling a component coupled toa computer in response to the gesture signal.
 45. The method of claim44, wherein the imaging system comprises a plurality of opticaldetectors, wherein at least two of the optical detectors are wavefrontcoded cameras comprising a wavefront coding optical element.
 46. Themethod of claim 44, wherein the imaging comprises generating wavefrontcoded images of the body.
 47. The method of claim 44, wherein theimaging system comprises a plurality of optical detectors, wherein atleast two of the optical detectors are wavefront coded camerascomprising a phase mask that increases a depth of focus of the imaging.48. The method of claim 44, wherein the gesture data comprisesfocus-resolved range data of the body within the depth of field.
 49. Themethod of claim 48, wherein the focus-resolved range data of the bodywithin the depth of field is derived from an output of the imagingsystem.
 50. The method of claim 44, wherein the gesture data comprisesfocus-resolved position data of the body within the depth of field. 51.The method of claim 50, wherein the focus-resolved position data of thebody within the depth of field is derived from an output of the imagingsystem.
 52. The method of claim 44, comprising generating modulationtransfer functions and point spread functions that are invariant to adistance between the body and the imaging system.
 53. The method ofclaim 44, comprising generating modulation transfer functions and pointspread functions that are invariant with respect to defocus.
 54. Themethod of claim 44, comprising generating intermediate images by codingimages gathered by the wavefront coded cameras.
 55. The method of claim54, wherein the intermediate images are blurred.
 56. The method of claim54, wherein the intermediate images are insensitive to changes in thebody or a plurality of optical detectors of the imaging system thatinclude defocus aberrations.
 57. The method of claim 44, wherein thegesture data is three-space location data representing the gesture. 58.The method of claim 44, wherein the detecting includes detecting alocation of the body.
 59. The method of claim 44, wherein the detectingincludes detecting an orientation of the body.
 60. The method of claim44, wherein the detecting includes detecting motion of the body.
 61. Themethod of claim 44, wherein the detecting comprises identifying thegesture, wherein the identifying includes identifying a pose and anorientation of a portion of the body.
 62. The method of claim 44,wherein the detecting includes detecting at least one of a first set ofappendages and a second set of appendages of the body.
 63. The method ofclaim 44, wherein the detecting includes dynamically detecting aposition of at least one tag.
 64. The method of claim 63, wherein thedetecting includes detecting position of a set of tags coupled to a partof the body.
 65. The method of claim 64, wherein each tag of the set oftags includes a pattern, wherein each pattern of each tag of the set oftags is different than any pattern of any remaining tag of the pluralityof tags.
 66. The method of claim 44, wherein the detecting includesdynamically detecting and locating a marker on the body.
 67. The methodof claim 66, wherein the detecting includes detecting position of a setof markers coupled to a part of the body.
 68. The method of claim 66,wherein the set of markers form a plurality of patterns on the body. 69.The method of claim 66, wherein the detecting includes detectingposition of a plurality of appendages of the body using a set of markerscoupled to each of the appendages.
 70. The method of claim 44, whereinthe translating comprises translating information of the gesture to agesture notation.
 71. The method of claim 70, wherein the gesturenotation represents a gesture vocabulary, and the gesture signalcomprises communications of the gesture vocabulary.
 72. The method ofclaim 71, wherein the gesture vocabulary represents in textual forminstantaneous pose states of kinematic linkages of the body.
 73. Themethod of claim 71, wherein the gesture vocabulary represents in textualform an orientation of kinematic linkages of the body.
 74. The method ofclaim 71, wherein the gesture vocabulary represents in textual form acombination of orientations of kinematic linkages of the body.
 75. Themethod of claim 71, wherein the gesture vocabulary includes a string ofcharacters that represent a state of kinematic linkages of the body. 76.The method of claim 75, wherein the kinematic linkage is at least onefirst appendage of the body.
 77. The method of claim 76, comprisingassigning each position in the string to a second appendage, the secondappendage connected to the first appendage.
 78. The method of claim 77,comprising assigning characters of a plurality of characters to each ofa plurality of positions of the second appendage.
 79. The method ofclaim 78, wherein the plurality of positions is established relative toa coordinate origin.
 80. The method of claim 79, comprising establishingthe coordinate origin using a position selected from a group consistingof an absolute position and orientation in space, a fixed position andorientation relative to the body irrespective of an overall position andheading of the body, and interactively in response to an action of thebody.
 81. The method of claim 78, comprising assigning characters of theplurality of characters to each of a plurality of orientations of thefirst appendage.
 82. The method of claim 76, wherein the detectingcomprises detecting when an extrapolated position of the body intersectsvirtual space, wherein the virtual space comprises space depicted on adisplay device coupled to the computer.
 83. The method of claim 82,wherein controlling the component comprises controlling a virtual objectin the virtual space when the extrapolated position intersects thevirtual object.
 84. The method of claim 83, wherein controlling thecomponent comprises controlling a position of the virtual object in thevirtual space in response to the extrapolated position in the virtualspace.
 85. The method of claim 83, wherein controlling the componentcomprises controlling attitude of the virtual object in the virtualspace in response to the gesture.
 86. The method of claim 44, comprisingcontrolling scaling of the detecting and controlling to generatecoincidence between virtual space and physical space, wherein thevirtual space comprises space depicted on a display device coupled tothe processor, wherein the physical space comprises space inhabited bythe body.
 87. The method of claim 86, comprising translating scale,angle, depth, and dimension between the virtual space and the physicalspace as appropriate to at least one application coupled to theprocessor.
 88. The method of claim 86, comprising controlling at leastone virtual object in the virtual space in response to movement of atleast one physical object in the physical space.
 89. The method of claim44, wherein the controlling includes controlling a function of anapplication hosted on the processor.
 90. The method of claim 44, whereinthe controlling includes controlling a component displayed on theprocessor.